Cyclic Linear Regression Normalization on MA Transformed Data
Source:R/NormalizationMethods.R
rlrMACycNorm.Rd
No reference, but MA transformation and normalization of samples is done pairwise between two samples with A = average of two samples and M = difference. The process is iterated through all samples pairs. Log2 data should be taken as input (on_raw = FALSE).
Arguments
- se
SummarizedExperiment containing all necessary information of the proteomic dataset
- ain
String which assay should be used as input
- aout
String which assay should be used to save normalized data
- on_raw
Boolean specifying whether normalization should be performed on raw or log2-scaled data
- iterations
Number of cyclic iterations to be performed
Examples
data(tuberculosis_TMT_se)
tuberculosis_TMT_se <- rlrMACycNorm(tuberculosis_TMT_se, ain = "log2",
aout = "RlrMACyc", on_raw = FALSE, iterations=3)
#> Warning: 'rlm' failed to converge in 20 steps
#> Warning: 'rlm' failed to converge in 20 steps
#> Warning: 'rlm' failed to converge in 20 steps
#> Warning: 'rlm' failed to converge in 20 steps
#> Warning: 'rlm' failed to converge in 20 steps
#> Warning: 'rlm' failed to converge in 20 steps
#> Warning: 'rlm' failed to converge in 20 steps
#> Warning: 'rlm' failed to converge in 20 steps
#> Warning: 'rlm' failed to converge in 20 steps
#> Warning: 'rlm' failed to converge in 20 steps
#> Warning: 'rlm' failed to converge in 20 steps
#> Warning: 'rlm' failed to converge in 20 steps
#> Warning: 'rlm' failed to converge in 20 steps
#> Warning: 'rlm' failed to converge in 20 steps
#> Warning: 'rlm' failed to converge in 20 steps